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Upload folder using huggingface_hub

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README.md CHANGED
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  ---
 
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  license: mit
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ library_name: pytorch
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  license: mit
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+ tags:
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+ - computer-vision
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+ - floorplan-reconstruction
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+ - pytorch
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+ - raster2seq
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  ---
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+
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+ # Raster2Seq Checkpoints
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+
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+ This repository hosts Raster2Seq PyTorch checkpoints for polygon sequence generation from rasterized floorplans. Each checkpoint is stored in its own subfolder so users can download all checkpoints or only the subfolder they need.
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+
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+ ## Available Checkpoints
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+
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+ | Checkpoint key | Dataset | RoomF1 | Subfolder |
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+ | --- | --- | ---: | --- |
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+ | `s3d-bw` | Structured3D-B | 99.6 | `s3d-bw/` |
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+ | `cubicasa5k` | CubiCasa5K | 88.7 | `cubicasa5k/` |
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+ | `raster2graph` | Raster2Graph | 97.0 | `raster2graph/` |
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+ | `s3d-density` | Structured3D-DensityMap | 99.1 | `s3d-density/` |
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+
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+ ## Download All Checkpoints
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+
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+ ```python
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+ from huggingface_hub import snapshot_download
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+
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+ local_repo = snapshot_download(repo_id="haopt/Raster2Seq")
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+ ```
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+
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+ ## Download One Checkpoint Subfolder
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+
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+ ```python
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+ from huggingface_hub import snapshot_download
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+
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+ local_repo = snapshot_download(
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+ repo_id="haopt/Raster2Seq",
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+ allow_patterns="cubicasa5k/*",
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+ )
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+ ```
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+
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+ ## Raster2Seq Helper
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+
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+ With the Raster2Seq codebase, users can load by alias:
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+
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+ ```bash
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+ python eval.py --checkpoint hf:cubicasa5k ...
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+ ```
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+
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+ or download directly:
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+
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+ ```python
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+ from raster2seq_hub import download_checkpoint
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+
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+ ckpt_path = download_checkpoint("cubicasa5k")
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+ ```
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+
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+ The helper downloads the matching subfolder and returns the cached `checkpoint.pth` path.
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+
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+ Hugging Face repository IDs use the form `namespace/repo`. Dataset-specific choices are represented by subfolders, not by repo IDs such as `Raster2Seq/cubicasa5k`.
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cubicasa5k/config.json ADDED
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+ {
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+ "checkpoint_key": "cubicasa5k",
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+ "name": "CubiCasa5K",
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+ "checkpoint_filename": "checkpoint.pth",
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+ "source_local_filename": "cc5k_sem_res256_ep0499.pth",
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+ "metrics": {
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+ "room_f1": 88.7
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+ },
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+ "inference_args": {
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+ "dataset_name": "cubicasa",
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+ "dataset_root": "data/coco_cubicasa5k_nowalls_v4-1_refined/",
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+ "eval_set": "test",
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+ "output_dir": "eval_outputs/cc5k_sem_results/",
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+ "semantic_classes": 12,
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+ "input_channels": 3,
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+ "poly2seq": true,
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+ "seq_len": 512,
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+ "num_bins": 32,
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+ "disable_poly_refine": true,
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+ "dec_attn_concat_src": true,
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+ "ema4eval": true,
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+ "use_anchor": true,
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+ "per_token_sem_loss": true,
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+ "save_pred": true
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+ }
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+ }
manifest.json ADDED
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+ {
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+ "repo_id": "haopt/Raster2Seq",
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+ "checkpoint_filename": "checkpoint.pth",
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+ "checkpoints": {
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+ "s3d-bw": {
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+ "name": "Structured3D-B",
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+ "subfolder": "s3d-bw",
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+ "local_filename": "s3dbw_sem_res256_ep0449.pth",
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+ "room_f1": 99.6,
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+ "config": "s3d-bw/config.json"
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+ },
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+ "cubicasa5k": {
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+ "name": "CubiCasa5K",
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+ "subfolder": "cubicasa5k",
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+ "local_filename": "cc5k_sem_res256_ep0499.pth",
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+ "room_f1": 88.7,
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+ "config": "cubicasa5k/config.json"
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+ },
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+ "raster2graph": {
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+ "name": "Raster2Graph",
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+ "subfolder": "raster2graph",
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+ "local_filename": "r2g_sem_res256_ep0549.pth",
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+ "room_f1": 97.0,
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+ "config": "raster2graph/config.json"
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+ },
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+ "s3d-density": {
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+ "name": "Structured3D-DensityMap",
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+ "subfolder": "s3d-density",
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+ "local_filename": "s3dd_sem_res256_ep0699.pth",
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+ "room_f1": 99.1,
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+ "config": "s3d-density/config.json"
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+ }
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+ },
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+ "aliases": {
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+ "s3d": "s3d-bw",
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+ "structured3d": "s3d-bw",
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+ "structured3d-b": "s3d-bw",
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+ "s3dbw": "s3d-bw",
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+ "cc5k": "cubicasa5k",
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+ "cubicasa": "cubicasa5k",
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+ "r2g": "raster2graph",
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+ "s3dd": "s3d-density",
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+ "structured3d-density": "s3d-density"
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+ }
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+ }
raster2graph/checkpoint.pth ADDED
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raster2graph/config.json ADDED
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+ {
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+ "checkpoint_key": "raster2graph",
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+ "name": "Raster2Graph",
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+ "checkpoint_filename": "checkpoint.pth",
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+ "source_local_filename": "r2g_sem_res256_ep0549.pth",
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+ "metrics": {
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+ "room_f1": 97.0
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+ },
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+ "inference_args": {
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+ "dataset_name": "r2g",
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+ "dataset_root": "data/R2G_hr_dataset_processed_v1",
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+ "eval_set": "test",
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+ "output_dir": "eval_outputs/r2g_sem_results",
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+ "semantic_classes": 13,
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+ "input_channels": 3,
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+ "poly2seq": true,
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+ "seq_len": 512,
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+ "num_bins": 32,
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+ "disable_poly_refine": true,
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+ "dec_attn_concat_src": true,
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+ "ema4eval": true,
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+ "use_anchor": true,
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+ "per_token_sem_loss": true,
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+ "save_pred": true
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+ }
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+ }
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+ "checkpoint_key": "s3d-bw",
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+ "name": "Structured3D-B",
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+ "checkpoint_filename": "checkpoint.pth",
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+ "source_local_filename": "s3dbw_sem_res256_ep0449.pth",
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+ "metrics": {
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+ "room_f1": 99.6
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+ },
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+ "inference_args": {
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+ "dataset_name": "stru3d",
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+ "dataset_root": "data/coco_s3d_bw",
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+ "eval_set": "test",
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+ "output_dir": "eval_outputs/s3dbw_sem_results",
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+ "semantic_classes": 19,
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+ "input_channels": 3,
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+ "poly2seq": true,
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+ "seq_len": 512,
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+ "num_bins": 32,
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+ "disable_poly_refine": true,
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+ "dec_attn_concat_src": true,
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+ "ema4eval": true,
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+ "use_anchor": true,
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+ "per_token_sem_loss": true,
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+ "save_pred": true
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+ }
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+ }
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s3d-density/config.json ADDED
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+ {
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+ "checkpoint_key": "s3d-density",
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+ "name": "Structured3D-DensityMap",
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+ "checkpoint_filename": "checkpoint.pth",
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+ "source_local_filename": "s3dd_sem_res256_ep0699.pth",
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+ "metrics": {
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+ "room_f1": 99.1
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+ },
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+ "inference_args": {
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+ "dataset_name": "stru3d",
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+ "dataset_root": "data/stru3d",
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+ "eval_set": "test",
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+ "output_dir": "eval_outputs/s3dd_sem_results",
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+ "semantic_classes": 19,
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+ "input_channels": 1,
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+ "poly2seq": true,
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+ "seq_len": 512,
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+ "num_bins": 32,
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+ "disable_poly_refine": true,
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+ "use_anchor": true,
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+ "per_token_sem_loss": true,
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+ "save_pred": true
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+ }
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+ }